Structural Balance of Social Internet of Things Networks with Ambiguous Relationships

In the social Internet of Things, social networks can be built among smart objects or between smart objects and people just like human beings. One of the factors that determines the effect and efficiency of service matching in SIoT is the structure of social networks. In this paper, we exploit the theory of structural balance in signed networks to optimize SIoT network structures to provide a friendly and stable environment as well as a solid foundation for service matching of social Internet of Things. Next, besides being friends or enemies, which are traditional relationships of structural balance or structural changes in signed networks, in our research, we introduce the ambiguous relationship which is the certain state between the hostile and the friendly status and we discuss the meaning and significance of ambiguous relationship in dynamical changes of structural balance in SIoT networks. Based on previous studies, we apply an enhanced objective function and a modified approach concerning the ambiguous relationship towards the dynamical change process. Experiments show that our approach is more effective and efficient than former studies in optimizing dynamical evolution of structural balance in signed networks of SIoT.

H2O: Secure Interactions in IoT via Behavioral Fingerprinting

Sharing data and services in the Internet of Things (IoT) can give rise to significant security concerns with information being sensitive and vulnerable to attacks. In such an environment, objects can be either public resources or owned by humans. For this reason, the need of monitoring the reliability of all involved actors, both persons and smart objects, assuring that they really are who they claim to be, is becoming an essential property of the IoT, with the increase in the pervasive adoption of such a paradigm. In this paper, we tackle this problem by proposing a new framework, called H2O (Human to Object). Our solution is able to continuously authenticate an entity in the network, providing a reliability assessment mechanism based on behavioral fingerprinting. A detailed security analysis evaluates the robustness of the proposed protocol; furthermore, a performance analysis shows the feasibility of our approach.